התחל

MATH

SimilarityMeasuresLibrary "SimilarityMeasures" Similarity measures are statistical methods used to quantify the distance between different data sets or strings. There are various types of similarity measures, including those that compare: - data points (SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl), - strings (Edit(Levenshtein), Lee, Hamming, Jaro), - probability distributions (Mahalanobis, Fidelity, Bhattacharyya, Hellinger), - sets (Kumar Hassebrook, Jaccard, Sorensen, Chi Square). --- These measures are used in various fields such as data analysis, machine learning, and pattern recognition. They help to compare and analyze similarities and differences between different data sets or strings, which can be useful for making predictions, classifications, and decisions. --- References: en.wikipedia.org cran.r-project.org numerics.mathdotnet.com github.com github.com github.com Encyclopedia of Distances, doi.org ssd(p, q)   Sum of squared difference for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Measure of distance that calculates the squared euclidean distance. euclidean(p, q)   Euclidean distance for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Measure of distance that calculates the straight-line (or Euclidean). manhattan(p, q)   Manhattan distance for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Measure of absolute differences between both points. minkowski(p, q, p_value)   Minkowsky Distance for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.      p_value (float) : `float` P value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.   Returns: Measure of similarity in the normed vector space. chebyshev(p, q)   Chebyshev distance for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Measure of maximum absolute difference. correlation(p, q)   Correlation distance for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Measure of maximum absolute difference. cosine(p, q)   Cosine distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The Cosine distance between vectors `p` and `q`. --- angiogenesis.dkfz.de camberra(p, q)   Camberra distance for N dimensions.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Weighted measure of absolute differences between both points. mae(p, q)   Mean absolute error is a normalized version of the sum of absolute difference (manhattan).   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Mean absolute error of vectors `p` and `q`. mse(p, q)   Mean squared error is a normalized version of the sum of squared difference.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Mean squared error of vectors `p` and `q`. lorentzian(p, q)   Lorentzian distance between provided vectors.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Lorentzian distance of vectors `p` and `q`. --- angiogenesis.dkfz.de intersection(p, q)   Intersection distance between provided vectors.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Intersection distance of vectors `p` and `q`. --- angiogenesis.dkfz.de penrose(p, q)   Penrose Shape distance between provided vectors.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Penrose shape distance of vectors `p` and `q`. --- angiogenesis.dkfz.de meehl(p, q)   Meehl distance between provided vectors.   Parameters:      p (float ) : `array` Vector with first numeric distribution.      q (float ) : `array` Vector with second numeric distribution.   Returns: Meehl distance of vectors `p` and `q`. --- angiogenesis.dkfz.de edit(x, y)   Edit (aka Levenshtein) distance for indexed strings.   Parameters:      x (int ) : `array` Indexed array.      y (int ) : `array` Indexed array.   Returns: Number of deletions, insertions, or substitutions required to transform source string into target string. --- generated description: The Edit distance is a measure of similarity used to compare two strings. It is defined as the minimum number of operations (insertions, deletions, or substitutions) required to transform one string into another. The operations are performed on the characters of the strings, and the cost of each operation depends on the specific algorithm used. The Edit distance is widely used in various applications such as spell checking, text similarity, and machine translation. It can also be used for other purposes like finding the closest match between two strings or identifying the common prefixes or suffixes between them. --- github.com www.red-gate.com planetcalc.com lee(x, y, dsize)   Distance between two indexed strings of equal length.   Parameters:      x (int ) : `array` Indexed array.      y (int ) : `array` Indexed array.      dsize (int) : `int` Dictionary size.   Returns: Distance between two strings by accounting for dictionary size. --- www.johndcook.com hamming(x, y)   Distance between two indexed strings of equal length.   Parameters:      x (int ) : `array` Indexed array.      y (int ) : `array` Indexed array.   Returns: Length of different components on both sequences. --- en.wikipedia.org jaro(x, y)   Distance between two indexed strings.   Parameters:      x (int ) : `array` Indexed array.      y (int ) : `array` Indexed array.   Returns: Measure of two strings' similarity: the higher the value, the more similar the strings are. The score is normalized such that `0` equates to no similarities and `1` is an exact match. --- rosettacode.org mahalanobis(p, q, VI)   Mahalanobis distance between two vectors with population inverse covariance matrix.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.      VI (matrix) : `matrix` Inverse of the covariance matrix.   Returns: The mahalanobis distance between vectors `p` and `q`. --- people.revoledu.com stat.ethz.ch docs.scipy.org fidelity(p, q)   Fidelity distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The Bhattacharyya Coefficient between vectors `p` and `q`. --- en.wikipedia.org bhattacharyya(p, q)   Bhattacharyya distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The Bhattacharyya distance between vectors `p` and `q`. --- en.wikipedia.org hellinger(p, q)   Hellinger distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The hellinger distance between vectors `p` and `q`. --- en.wikipedia.org jamesmccaffrey.wordpress.com kumar_hassebrook(p, q)   Kumar Hassebrook distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The Kumar Hassebrook distance between vectors `p` and `q`. --- github.com jaccard(p, q)   Jaccard distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The Jaccard distance between vectors `p` and `q`. --- github.com sorensen(p, q)   Sorensen distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.   Returns: The Sorensen distance between vectors `p` and `q`. --- people.revoledu.com chi_square(p, q, eps)   Chi Square distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.      eps (float)   Returns: The Chi Square distance between vectors `p` and `q`. --- uw.pressbooks.pub stats.stackexchange.com www.itl.nist.gov kulczynsky(p, q, eps)   Kulczynsky distance between provided vectors.   Parameters:      p (float ) : `array` 1D Vector.      q (float ) : `array` 1D Vector.      eps (float)   Returns: The Kulczynsky distance between vectors `p` and `q`. --- github.com
ספריית Pine Script™
מאת ‎RicardoSantos‎
11
FunctionMatrixCovarianceLibrary "FunctionMatrixCovariance" In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. Intuitively, the covariance matrix generalizes the notion of variance to multiple dimensions. As an example, the variation in a collection of random points in two-dimensional space cannot be characterized fully by a single number, nor would the variances in the `x` and `y` directions contain all of the necessary information; a `2 × 2` matrix would be necessary to fully characterize the two-dimensional variation. Any covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the covariance of each element with itself). The covariance matrix of a random vector `X` is typically denoted by `Kxx`, `Σ` or `S`. ~wikipedia. method cov(M, bias)   Estimate Covariance matrix with provided data.   Namespace types: matrix   Parameters:      M (matrix) : `matrix` Matrix with vectors in column order.      bias (bool)   Returns: Covariance matrix of provided vectors. --- en.wikipedia.org numpy.org
ספריית Pine Script™
מאת ‎RicardoSantos‎
Extended Moving Average (MA) LibraryThis Extended Moving Average Library is a sophisticated and comprehensive tool for traders seeking to expand their arsenal of moving averages for more nuanced and detailed technical analysis. The library contains various types of moving averages, each with two versions - one that accepts a simple constant length parameter and another that accepts a series or changing length parameter. This makes the library highly versatile and suitable for a wide range of strategies and trading styles. Moving Averages Included: Simple Moving Average (SMA): This is the most basic type of moving average. It calculates the average of a selected range of prices, typically closing prices, by the number of periods in that range. Exponential Moving Average (EMA): This type of moving average gives more weight to the latest data and is thus more responsive to new price information. This can help traders to react faster to recent price changes. Double Exponential Moving Average (DEMA): This is a composite of a single exponential moving average, a double exponential moving average, and an exponential moving average of a triple exponential moving average. It aims to eliminate lag, which is a key drawback of using moving averages. Jurik Moving Average (JMA): This is a versatile and responsive moving average that can be adjusted for market speed. It is designed to stay balanced and responsive, regardless of how long or short it is. Kaufman's Adaptive Moving Average (KAMA): This moving average is designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. Smoothed Moving Average (SMMA): This type of moving average applies equal weighting to all observations and smooths out the data. Triangular Moving Average (TMA): This is a double smoothed simple moving average, calculated by averaging the simple moving averages of a dataset. True Strength Force (TSF): This is a moving average of the linear regression line, a statistical tool used to predict future values from past values. Volume Moving Average (VMA): This is a simple moving average of a volume, which can help to identify trends in volume. Volume Adjusted Moving Average (VAMA): This moving average adjusts for volume and can be more responsive to volume changes. Zero Lag Exponential Moving Average (ZLEMA): This type of moving average aims to eliminate the lag in traditional EMAs, making it more responsive to recent price changes. Selector: The selector function allows users to easily select and apply any of the moving averages included in the library inside their strategy. This library provides a broad selection of moving averages to choose from, allowing you to experiment with different types and find the one that best suits your trading strategy. By providing both simple and series versions for each moving average, this library offers great flexibility, enabling users to pass both constant and changing length parameters as needed.
ספריית Pine Script™
מאת ‎gotbeatz26107‎
4
ta_mLibrary "ta_m" This library is a Pine Script™ programmer’s tool containing calcs for my oscillators and some helper functions. upDnIntrabarVolumesByPolarity()   Determines if the volume for an intrabar is up or down.   Returns: ( ) A tuple of two values, one of which contains the bar's volume. `upVol` is the positive volume of up bars. `dnVol` is the negative volume of down bars. Note that when this function is designed to be called with `request.security_lower_tf()`, which will return a tuple of "array" arrays containing up and dn volume for all the intrabars in a chart bar. upDnIntrabarVolumesByPrice()   Determines if the intrabar volume is up or down   Returns: ( ) A tuple of two values, one of which contains the bar's volume. `upVol` is the positive volume of up bars. `dnVol` is the negative volume of down bars. Note that when this function is designed to be called with `request.security_lower_tf()`, which will return a tuple of "array" arrays containing up and dn volume for all the intrabars in a chart bar.
ספריית Pine Script™
מאת ‎vikku‎
LibrarySupertrendLibrary "LibrarySupertrend" selective_ma(condition, source, length)   Parameters:      condition (bool)      source (float)      length (int) trendUp(source)   Parameters:      source (float) smoothrng(source, sampling_period, range_mult)   Parameters:      source (float)      sampling_period (simple int)      range_mult (float) rngfilt(source, smoothrng)   Parameters:      source (float)      smoothrng (float) fusion(overallLength, rsiLength, mfiLength, macdLength, cciLength, tsiLength, rviLength, atrLength, adxLength)   Parameters:      overallLength (simple int)      rsiLength (simple int)      mfiLength (simple int)      macdLength (simple int)      cciLength (simple int)      tsiLength (simple int)      rviLength (simple int)      atrLength (simple int)      adxLength (simple int) zonestrength(amplitude, wavelength)   Parameters:      amplitude (int)      wavelength (simple int) atr_anysource(source, atr_length)   Parameters:      source (float)      atr_length (simple int) supertrend_anysource(source, factor, atr_length)   Parameters:      source (float)      factor (float)      atr_length (simple int)
ספריית Pine Script™
מאת ‎Yungbluh‎
lib_drawing_compositesLibrary "lib_drawing_composites" methods to draw and manage composite obejects. Based on Trendoscope's added Triangle and Polygon composite objects, fixed tostring method output to be actual json method tostring(this, format_date, format, tz, pretty)   Converts lib_drawing_types/LineProperties object to a json string representation   Namespace types: D.Point   Parameters:      this (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) : lib_drawing_types/LineProperties object      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) : if true adds a line feed after every property and a space before properties (default: true)   Returns: string representation of lib_drawing_types/LineProperties method tostring(this, pretty)   Converts lib_drawing_types/LabelProperties object to a json string representation   Namespace types: D.LineProperties   Parameters:      this (LineProperties type from HeWhoMustNotBeNamed/DrawingTypes/2) : lib_drawing_types/LabelProperties object      pretty (simple bool) : if true adds a line feed after every property and a space before properties (default: true)   Returns: string representation of lib_drawing_types/LabelProperties method tostring(this, format_date, format, tz, pretty)   Converts lib_drawing_types/BoxProperties object to a json string representation   Namespace types: D.Line   Parameters:      this (Line type from HeWhoMustNotBeNamed/DrawingTypes/2) : lib_drawing_types/BoxProperties object      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) : if true adds a line feed after every property and a space before properties (default: true)   Returns: string representation of lib_drawing_types/BoxProperties method tostring(this, pretty)   Converts lib_drawing_types/BoxText object to a json string representation   Namespace types: D.LabelProperties   Parameters:      this (LabelProperties type from HeWhoMustNotBeNamed/DrawingTypes/2) : lib_drawing_types/BoxText object      pretty (simple bool) : if true adds a line feed after every property and a space before properties (default: true)   Returns: string representation of lib_drawing_types/BoxText method tostring(this, format_date, format, tz, pretty)   Converts lib_drawing_types/TriangleProperties object to a json string representation   Namespace types: D.Label   Parameters:      this (Label type from HeWhoMustNotBeNamed/DrawingTypes/2) : lib_drawing_types/TriangleProperties object      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) : if true adds a line feed after every property and a space before properties (default: true)   Returns: string representation of lib_drawing_types/TriangleProperties method tostring(this, format_date, format, tz, pretty)   Namespace types: D.Linefill   Parameters:      this (Linefill type from HeWhoMustNotBeNamed/DrawingTypes/2)      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) method tostring(this, pretty)   Namespace types: D.BoxProperties   Parameters:      this (BoxProperties type from HeWhoMustNotBeNamed/DrawingTypes/2)      pretty (simple bool) method tostring(this, pretty)   Namespace types: D.BoxText   Parameters:      this (BoxText type from HeWhoMustNotBeNamed/DrawingTypes/2)      pretty (simple bool) method tostring(this, format_date, format, tz, pretty)   Namespace types: D.Box   Parameters:      this (Box type from HeWhoMustNotBeNamed/DrawingTypes/2)      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) method tostring(this, pretty)   Namespace types: DC.TriangleProperties   Parameters:      this (TriangleProperties type from robbatt/lib_drawing_composite_types/1)      pretty (simple bool) method tostring(this, format_date, format, tz, pretty)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1)      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) method tostring(this, format_date, format, tz, pretty)   Namespace types: DC.Trianglefill   Parameters:      this (Trianglefill type from robbatt/lib_drawing_composite_types/1)      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) method tostring(this, format_date, format, tz, pretty)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1)      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) method tostring(this, format_date, format, tz, pretty)   Namespace types: DC.Polygonfill   Parameters:      this (Polygonfill type from robbatt/lib_drawing_composite_types/1)      format_date (simple bool)      format (simple string)      tz (simple string)      pretty (simple bool) method delete(this)   Namespace types: DC.Trianglefill   Parameters:      this (Trianglefill type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Trianglefill   Parameters:      this (Trianglefill type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Polygonfill   Parameters:      this (Polygonfill type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1) method delete(this)   Namespace types: DC.Polygonfill   Parameters:      this (Polygonfill type from robbatt/lib_drawing_composite_types/1) method clear(this)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1) method clear(this)   Namespace types: DC.Trianglefill   Parameters:      this (Trianglefill type from robbatt/lib_drawing_composite_types/1) method clear(this)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1) method clear(this)   Namespace types: DC.Polygonfill   Parameters:      this (Polygonfill type from robbatt/lib_drawing_composite_types/1) method draw(this, is_polygon_section)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1)      is_polygon_section (bool) method draw(this)   Namespace types: DC.Trianglefill   Parameters:      this (Trianglefill type from robbatt/lib_drawing_composite_types/1) method draw(this, is_polygon)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1)      is_polygon (bool) method draw(this)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1) method draw(this)   Namespace types: DC.Trianglefill   Parameters:      this (Trianglefill type from robbatt/lib_drawing_composite_types/1) method draw(this)   Namespace types: DC.Polygonfill   Parameters:      this (Polygonfill type from robbatt/lib_drawing_composite_types/1) method draw(this)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1) method draw(this)   Namespace types: DC.Polygonfill   Parameters:      this (Polygonfill type from robbatt/lib_drawing_composite_types/1) method createCenter(this, other)   Namespace types: D.Point   Parameters:      this (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      other (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) method createCenter(this)   Namespace types: D.Point   Parameters:      this (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) method createCenter(this, other1, other2)   Namespace types: D.Point   Parameters:      this (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      other1 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      other2 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) method createLabel(this, labeltext, tooltip, properties)   Namespace types: D.Line   Parameters:      this (Line type from HeWhoMustNotBeNamed/DrawingTypes/2)      labeltext (string)      tooltip (string)      properties (LabelProperties type from HeWhoMustNotBeNamed/DrawingTypes/2) method createLabel(this, labeltext, tooltip, properties)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1)      labeltext (string)      tooltip (string)      properties (LabelProperties type from HeWhoMustNotBeNamed/DrawingTypes/2) method createTriangle(this, p2, p3, properties)   Namespace types: D.Point   Parameters:      this (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      p2 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      p3 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      properties (TriangleProperties type from robbatt/lib_drawing_composite_types/1) method createTrianglefill(this, fill_color, transparency)   Namespace types: DC.Triangle   Parameters:      this (Triangle type from robbatt/lib_drawing_composite_types/1)      fill_color (color)      transparency (int) method createPolygonfill(this, fill_color, transparency)   Namespace types: DC.Polygon   Parameters:      this (Polygon type from robbatt/lib_drawing_composite_types/1)      fill_color (color)      transparency (int) method createPolygon(points, properties)   Namespace types: D.Point   Parameters:      points (Point type from HeWhoMustNotBeNamed/DrawingTypes/2)      properties (TriangleProperties type from robbatt/lib_drawing_composite_types/1)
ספריית Pine Script™
מאת ‎robbatt‎
lib_drawing_composite_typesLibrary "lib_drawing_composite_types" User Defined Types for basic drawing structure. Other types and methods will be built on these. (added type Triangle and Polygon to ) TriangleProperties   TriangleProperties object   Fields:      border_color (series color) : Box border color. Default is color.blue      fill_color (series color) : Fill color      fill_transparency (series int)      border_width (series int) : Box border width. Default is 1      border_style (series string) : Box border style. Default is line.style_solid      xloc (series string) : defines if drawing needs to be done based on bar index or time. default is xloc.bar_index Triangle   Triangle object   Fields:      p1 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) : point one      p2 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) : point two      p3 (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) : point three      properties (TriangleProperties) : Triangle properties      l12 (series line) : line object created      l23 (series line) : line object created      l31 (series line) : line object created Trianglefill   Trianglefill object   Fields:      triangle (Triangle) : to create a linefill for      fill_color (series color) : Fill color      transparency (series int) : Fill transparency range from 0 to 100      object (series linefill) : linefill object created Polygon   Polygon object   Fields:      center (Point type from HeWhoMustNotBeNamed/DrawingTypes/2) : Point that triangles are using as common center      triangles (Triangle ) : an array of triangles that form the Polygon Polygonfill   Polygonfill object   Fields:      _polygon (Polygon) : to create a fill for      _fills (Trianglefill ) : an array of Trianglefill objects that match the array of triangles in _polygon
ספריית Pine Script™
מאת ‎robbatt‎
Risk ManagementLibrary "RiskManagement" This library keeps your money in check, and is used for testing and later on webhook-applications too. It has four volatility functions and two of them can be used to calculate a Stop-Loss, like Average True Range. It also can calculate Position Size, and the Risk Reward Ratio. But those calculations don't take leverage into account. position_size(portfolio, risk, entry, stop_loss, use_leverage, qty_as_integer)   This function calculates the definite amount of contracts/shares/units you should use to buy or sell. This value can used by `strategy.entry(qty)` for example.   Parameters:      portfolio (float) : This is the total amount of the currency you own, and is also used by strategy.initial_capital, for example. The amount is needed to calculate the maximum risk you are willing to take per trade.      risk (float) : This is the percentage of your Portfolio you willing to loose on a single trade. Possible values are between 0.1 and 100%. Same usecase with strategy(default_qty_type=strategy.percent_of_equity,default_qty_value=100), except its calculation the risk only.      entry (float) : This is the limit-/market-price for the investment. In other words: The price per contract/share/unit you willing to buy or sell.      stop_loss (float) : This is the limit-/market-price when to exit the trade, to minimize your losses.      use_leverage (bool) : This value is optional. When not used or when set to false then this function will let you invest your portfolio at max.      qty_as_integer (bool) : This value is optional. When set to true this function will return a value used with integers. The largest integer less than or equal to the given number. Because some Broker/Exchanges let you trade hole contracts/shares/units only.   Returns: float position_size_currency(portfolio, risk, entry, stop_loss)   This function calculates the definite amount of currency you should use when going long or short.   Parameters:      portfolio (float) : This is the total amount of the currency you own, and is also used by strategy.initial_capital, for example. The amount is needed to calculate the maximum risk you are willing to take per trade.      risk (float) : This is the percentage of your Portfolio you willing to loose on a single trade. For example: 1 is 100% and 0,01 is 1%. Default amount is 0.02 (2%).      entry (float) : This is the limit-/market-price for the current investment. In other words: The price per contract/share/units you willing to buy or sell.      stop_loss (float) : This is the limit-/market-price when to exit the trade, to minimize your losses.   Returns: float rrr(entry, stop_loss, take_profit)   This function calculates the Risk Reward Ratio. Common values are between 1.5 and 2.0 and you should not go lower except for very few special cases.   Parameters:      entry (float) : This is the limit-/market-price for the investment. In other words: The price per contract/share/unit you willing to buy or sell.      stop_loss (float) : This is the limit-/market-price when to exit the trade, to minimize your losses.      take_profit (float) : This is the limit-/market-price when to take profits.   Returns: float change_in_price(length)   This function calculates the difference between price now and close price of the candle 'n' bars before that. If prices are very volatile but closed where they began, then this method would show zero volatility. Over many calculations, this method returns a reasonable measure of volatility, but will always be lower than those using the highs and lows.   Parameters:      length (int) : The length is needed to determine how many candles/bars back should take into account.   Returns: float maximum_price_fluctuation(length)   This function measures volatility over most recent candles, which could be used as an estimate of risk. It may also be effective as the basis for a stop-loss or take-profit, like the ATR but it ignores the frequency of directional changes within the time interval. In other words: The difference between the highest high and lowest low over 'n' bars.   Parameters:      length (int) : The length is needed to determine how many candles/bars back should take into account.   Returns: float absolute_price_changes(length)   This function measures volatility over most recent close prices. This is excellent for comparing volatility. It includes both frequency and magnitude. In other words: Sum of differences between second to last close price and last close price as absolute value for 'n' bars.   Parameters:      length (int) : The length is needed to determine how many candles/bars back should take into account.   Returns: float annualized_volatility(length)   This function measures volatility over most recent close prices. Its the standard deviation of close over the past 'n' periods, times the square root of the number of periods in a year.   Parameters:      length (int) : The length is needed to determine how many candles/bars back should take into account.   Returns: float
ספריית Pine Script™
מאת ‎weak_hand‎
KernelFunctionsFiltersLibrary "KernelFunctionsFilters" This library provides filters for non-repainting kernel functions for Nadaraya-Watson estimator implementations made by @jdehorty. Filters include a smoothing formula and zero lag formula. You can find examples in the code. For more information check out the original library KernelFunctions. rationalQuadratic(_src, _lookback, _relativeWeight, startAtBar, _filter)   Parameters:      _src (float)      _lookback (simple int)      _relativeWeight (simple float)      startAtBar (simple int)      _filter (simple string) gaussian(_src, _lookback, startAtBar, _filter)   Parameters:      _src (float)      _lookback (simple int)      startAtBar (simple int)      _filter (simple string) periodic(_src, _lookback, _period, startAtBar, _filter)   Parameters:      _src (float)      _lookback (simple int)      _period (simple int)      startAtBar (simple int)      _filter (simple string) locallyPeriodic(_src, _lookback, _period, startAtBar, _filter)   Parameters:      _src (float)      _lookback (simple int)      _period (simple int)      startAtBar (simple int)      _filter (simple string) j(line1, line2)   Parameters:      line1 (float)      line2 (float)
ספריית Pine Script™
מאת ‎veryfid‎
6
Vector3Library "Vector3" Representation of 3D vectors and points. This structure is used to pass 3D positions and directions around. It also contains functions for doing common vector operations. Besides the functions listed below, other classes can be used to manipulate vectors and points as well. For example the Quaternion and the Matrix4x4 classes are useful for rotating or transforming vectors and points. ___ **Reference:** - github.com - github.com - github.com - www.movable-type.co.uk - docs.unity3d.com - referencesource.microsoft.com - github.com \ new(x, y, z)   Create a new `Vector3`.   Parameters:      x (float) : `float` Property `x` value, (optional, default=na).      y (float) : `float` Property `y` value, (optional, default=na).      z (float) : `float` Property `z` value, (optional, default=na).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .new(1.1, 1, 1) ``` from(value)   Create a new `Vector3` from a single value.   Parameters:      value (float) : `float` Properties positional value, (optional, default=na).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .from(1.1) ``` from_Array(values, fill_na)   Create a new `Vector3` from a list of values, only reads up to the third item.   Parameters:      values (float ) : `array` Vector property values.      fill_na (float) : `float` Parameter value to replace missing indexes, (optional, defualt=na).   Returns: `Vector3` Generated new vector. ___ **Notes:** - Supports any size of array, fills non available fields with `na`. ___ **Usage:** ``` .from_Array(array.from(1.1, fill_na=33)) .from_Array(array.from(1.1, 2, 3)) ``` from_Vector2(values)   Create a new `Vector3` from a `Vector2`.   Parameters:      values (Vector2 type from RicardoSantos/CommonTypesMath/1) : `Vector2` Vector property values.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .from:Vector2(.Vector2.new(1, 2.0)) ``` ___ **Notes:** - Type `Vector2` from CommonTypesMath library. from_Quaternion(values)   Create a new `Vector3` from a `Quaternion`'s `x, y, z` properties.   Parameters:      values (Quaternion type from RicardoSantos/CommonTypesMath/1) : `Quaternion` Vector property values.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .from_Quaternion(.Quaternion.new(1, 2, 3, 4)) ``` ___ **Notes:** - Type `Quaternion` from CommonTypesMath library. from_String(expression, separator, fill_na)   Create a new `Vector3` from a list of values in a formated string.   Parameters:      expression (string) : `array` String with the list of vector properties.      separator (string) : `string` Separator between entries, (optional, default=`","`).      fill_na (float) : `float` Parameter value to replace missing indexes, (optional, defualt=na).   Returns: `Vector3` Generated new vector. ___ **Notes:** - Supports any size of array, fills non available fields with `na`. - `",,"` Empty fields will be ignored. ___ **Usage:** ``` .from_String("1.1", fill_na=33)) .from_String("(1.1,, 3)") // 1.1 , 3.0, NaN // empty field will be ignored!! ``` back()   Create a new `Vector3` object in the form `(0, 0, -1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .back() ``` front()   Create a new `Vector3` object in the form `(0, 0, 1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .front() ``` up()   Create a new `Vector3` object in the form `(0, 1, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .up() ``` down()   Create a new `Vector3` object in the form `(0, -1, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .down() ``` left()   Create a new `Vector3` object in the form `(-1, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .left() ``` right()   Create a new `Vector3` object in the form `(1, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .right() ``` zero()   Create a new `Vector3` object in the form `(0, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .zero() ``` one()   Create a new `Vector3` object in the form `(1, 1, 1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .one() ``` minus_one()   Create a new `Vector3` object in the form `(-1, -1, -1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .minus_one() ``` unit_x()   Create a new `Vector3` object in the form `(1, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .unit_x() ``` unit_y()   Create a new `Vector3` object in the form `(0, 1, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .unit_y() ``` unit_z()   Create a new `Vector3` object in the form `(0, 0, 1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .unit_z() ``` nan()   Create a new `Vector3` object in the form `(na, na, na)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .nan() ``` random(max, min)   Generate a vector with random properties.   Parameters:      max (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Maximum defined range of the vector properties.      min (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Minimum defined range of the vector properties.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .random(.from(math.pi), .from(-math.pi)) ``` random(max)   Generate a vector with random properties (min set to 0.0).   Parameters:      max (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Maximum defined range of the vector properties.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .random(.from(math.pi)) ``` method copy(this)   Copy a existing `Vector3`   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .one().copy() ``` method i_add(this, other)   Modify a instance of a vector by adding a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_add(.up()) ``` method i_add(this, value)   Modify a instance of a vector by adding a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_add(3.2) ``` method i_subtract(this, other)   Modify a instance of a vector by subtracting a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_subtract(.down()) ``` method i_subtract(this, value)   Modify a instance of a vector by subtracting a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_subtract(3) ``` method i_multiply(this, other)   Modify a instance of a vector by multiplying a vector with it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_multiply(.left()) ``` method i_multiply(this, value)   Modify a instance of a vector by multiplying a vector with it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_multiply(3) ``` method i_divide(this, other)   Modify a instance of a vector by dividing it by another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_divide(.forward()) ``` method i_divide(this, value)   Modify a instance of a vector by dividing it by another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_divide(3) ``` method i_mod(this, other)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_mod(.back()) ``` method i_mod(this, value)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_mod(3) ``` method i_pow(this, exponent)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      exponent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Exponent Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_pow(.up()) ``` method i_pow(this, exponent)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      exponent (float) : `float` Exponent Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_pow(2) ``` method length_squared(this)   Squared length of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1)   Returns: `float` The squared length of this vector. ___ **Usage:** ``` a = .one().length_squared() ``` method magnitude_squared(this)   Squared magnitude of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` The length squared of this vector. ___ **Usage:** ``` a = .one().magnitude_squared() ``` method length(this)   Length of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` The length of this vector. ___ **Usage:** ``` a = .one().length() ``` method magnitude(this)   Magnitude of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` The Length of this vector. ___ **Usage:** ``` a = .one().magnitude() ``` method normalize(this, magnitude, eps)   Normalize a vector with a magnitude of 1(optional).   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      magnitude (float) : `float` Value to manipulate the magnitude of normalization, (optional, default=1.0).      eps (float)   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(33, 50, 100).normalize() // (x=0.283, y=0.429, z=0.858) a = .new(33, 50, 100).normalize(2) // (x=0.142, y=0.214, z=0.429) ``` method to_String(this, precision)   Converts source vector to a string format, in the form `"(x, y, z)"`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      precision (string) : `string` Precision format to apply to values (optional, default='').   Returns: `string` Formated string in a `"(x, y, z)"` format. ___ **Usage:** ``` a = .one().to_String("#.###") ``` method to_Array(this)   Converts source vector to a array format.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `array` List of the vector properties. ___ **Usage:** ``` a = .new(1, 2, 3).to_Array() ``` method to_Vector2(this)   Converts source vector to a Vector2 in the form `x, y`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector2` Generated new vector. ___ **Usage:** ``` a = .from(1).to_Vector2() ``` method to_Quaternion(this, w)   Converts source vector to a Quaternion in the form `x, y, z, w`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Sorce vector.      w (float) : `float` Property of `w` new value.   Returns: `Quaternion` Generated new vector. ___ **Usage:** ``` a = .from(1).to_Quaternion(w=1) ``` method add(this, other)   Add a vector to source vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).add(.unit_z()) ``` method add(this, value)   Add a value to each property of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).add(2.0) ``` add(value, other)   Add each property of a vector to a base value as a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(2) , b = .add(1.0, a) ``` method subtract(this, other)   Subtract vector from source vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).subtract(.left()) ``` method subtract(this, value)   Subtract a value from each property in source vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).subtract(2.0) ``` subtract(value, other)   Subtract each property in a vector from a base value and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .subtract(1.0, .right()) ``` method multiply(this, other)   Multiply a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).multiply(.up()) ``` method multiply(this, value)   Multiply each element in source vector with a value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).multiply(2.0) ``` multiply(value, other)   Multiply a value with each property in a vector and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .multiply(1.0, .new(1, 2, 1)) ``` method divide(this, other)   Divide a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).divide(.from(2)) ``` method divide(this, value)   Divide each property in a vector by a value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).divide(2.0) ``` divide(value, other)   Divide a base value by each property in a vector and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .divide(1.0, .from(2)) ``` method mod(this, other)   Modulo a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).mod(.from(2)) ``` method mod(this, value)   Modulo each property in a vector by a value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).mod(2.0) ``` mod(value, other)   Modulo a base value by each property in a vector and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .mod(1.0, .from(2)) ``` method negate(this)   Negate a vector in the form `(zero - this)`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .one().negate() ``` method pow(this, other)   Modulo a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(2).pow(.from(3)) ``` method pow(this, exponent)   Raise the vector elements by a exponent.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      exponent (float) : `float` The exponent to raise the vector by.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).pow(2.0) ``` pow(value, exponent)   Raise value into a vector raised by the elements in exponent vector.   Parameters:      value (float) : `float` Base value.      exponent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The exponent to raise the vector of base value by.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .pow(1.0, .from(2)) ``` method sqrt(this)   Square root of the elements in a vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).sqrt() ``` method abs(this)   Absolute properties of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).abs() ``` method max(this)   Highest property of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` Highest value amongst the vector properties. ___ **Usage:** ``` a = .new(1, 2, 3).max() ``` method min(this)   Lowest element of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` Lowest values amongst the vector properties. ___ **Usage:** ``` a = .new(1, 2, 3).min() ``` method floor(this)   Floor of vector a.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).floor() ``` method ceil(this)   Ceil of vector a.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).ceil() ``` method round(this)   Round of vector elements.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).round() ``` method round(this, precision)   Round of vector elements to n digits.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      precision (int) : `int` Number of digits to round the vector elements.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).round(1) // 1.3, 1.7, 2 ``` method fractional(this)   Fractional parts of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1.337).fractional() // 0.337 ``` method dot_product(this, other)   Dot product of two vectors.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `float` Dot product. ___ **Usage:** ``` a = .from(2).dot_product(.left()) ``` method cross_product(this, other)   Cross product of two vectors.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).cross_produc(.right()) ``` method scale(this, scalar)   Scale vector by a scalar value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      scalar (float) : `float` Value to scale the the vector by.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).scale(2) ``` method rescale(this, magnitude)   Rescale a vector to a new magnitude.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      magnitude (float) : `float` Value to manipulate the magnitude of normalization.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(20).rescale(1) ``` method equals(this, other)   Compares two vectors.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).equals(.one()) ``` method sin(this)   Sine of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).sin() ``` method cos(this)   Cosine of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).cos() ``` method tan(this)   Tangent of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).tan() ``` vmax(a, b)   Highest elements of the properties from two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmax(.one(), .from(2)) ``` vmax(a, b, c)   Highest elements of the properties from three vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      c (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmax(.new(0.1, 2.5, 3.4), .from(2), .from(3)) ``` vmin(a, b)   Lowest elements of the properties from two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmin(.one(), .from(2)) ``` vmin(a, b, c)   Lowest elements of the properties from three vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      c (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmin(.one(), .from(2), .new(3.3, 2.2, 0.5)) ``` distance(a, b)   Distance between vector `a` and `b`.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = distance(.from(3), .unit_z()) ``` clamp(a, min, max)   Restrict a vector between a min and max vector.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      min (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Minimum boundary vector.      max (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Maximum boundary vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .clamp(a=.new(2.9, 1.5, 3.9), min=.from(2), max=.new(2.5, 3.0, 3.5)) ``` clamp_magnitude(a, radius)   Vector with its magnitude clamped to a radius.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.object, vector with properties that should be restricted to a radius.      radius (float) : `float` Maximum radius to restrict magnitude of vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .clamp_magnitude(.from(21), 7) ``` lerp_unclamped(a, b, rate)   `Unclamped` linearly interpolates between provided vectors by a rate.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      rate (float) : `float` Rate of interpolation, range(0 > 1) where 0 == source vector and 1 == target vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .lerp_unclamped(.from(1), .from(2), 1.2) ``` lerp(a, b, rate)   Linearly interpolates between provided vectors by a rate.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      rate (float) : `float` Rate of interpolation, range(0 > 1) where 0 == source vector and 1 == target vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = lerp(.one(), .from(2), 0.2) ``` herp(start, start_tangent, end, end_tangent, rate)   Hermite curve interpolation between provided vectors.   Parameters:      start (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Start vector.      start_tangent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Start vector tangent.      end (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` End vector.      end_tangent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` End vector tangent.      rate (int) : `float` Rate of the movement from `start` to `end` to get position, should be range(0 > 1).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` s = .new(0, 0, 0) , st = .new(0, 1, 1) e = .new(1, 2, 2) , et = .new(-1, -1, 3) h = .herp(s, st, e, et, 0.3) ``` ___ **Reference:** en.m.wikibooks.org herp_2(a, b, rate)   Hermite curve interpolation between provided vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      rate (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Rate of the movement per component from `start` to `end` to get position, should be range(0 > 1).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` h = .herp_2(.one(), .new(0.1, 3, 2), 0.6) ``` noise(a)   3D Noise based on Morgan McGuire @morgan3d   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = noise(.one()) ``` ___ **Reference:** - thebookofshaders.com - www.shadertoy.com rotate(a, axis, angle)   Rotate a vector around a axis.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      axis (string) : `string` The plane to rotate around, `option="x", "y", "z"`.      angle (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate(.from(3), 'y', math.toradians(45.0)) ``` rotate_x(a, angle)   Rotate a vector on a fixed `x`.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      angle (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate_x(.from(3), math.toradians(90.0)) ``` rotate_y(a, angle)   Rotate a vector on a fixed `y`.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      angle (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate_y(.from(3), math.toradians(90.0)) ``` rotate_yaw_pitch(a, yaw, pitch)   Rotate a vector by yaw and pitch values.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      yaw (float) : `float` Angle in radians.      pitch (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate_yaw_pitch(.from(3), math.toradians(90.0), math.toradians(45.0)) ``` project(a, normal, eps)   Project a vector off a plane defined by a normal.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      normal (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The normal of the surface being reflected off.      eps (float) : `float` Minimum resolution to void division by zero (default=0.000001).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .project(.one(), .down()) ``` project_on_plane(a, normal, eps)   Projects a vector onto a plane defined by a normal orthogonal to the plane.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      normal (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The normal of the surface being reflected off.      eps (float) : `float` Minimum resolution to void division by zero (default=0.000001).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .project_on_plane(.one(), .left()) ``` project_to_2d(a, camera_position, camera_target)   Project a vector onto a two dimensions plane.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      camera_position (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Camera position.      camera_target (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Camera target plane position.   Returns: `Vector2` Generated new vector. ___ **Usage:** ``` a = .project_to_2d(.one(), .new(2, 2, 3), .zero()) ``` reflect(a, normal)   Reflects a vector off a plane defined by a normal.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      normal (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The normal of the surface being reflected off.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .reflect(.one(), .right()) ``` angle(a, b, eps)   Angle in degrees between two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      eps (float) : `float` Minimum resolution to void division by zero (default=1.0e-15).   Returns: `float` Angle value in degrees. ___ **Usage:** ``` a = .angle(.one(), .up()) ``` angle_signed(a, b, axis)   Signed angle in degrees between two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      axis (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Axis vector.   Returns: `float` Angle value in degrees. ___ **Usage:** ``` a = .angle_signed(.one(), .left(), .down()) ``` ___ **Notes:** - The smaller of the two possible angles between the two vectors is returned, therefore the result will never be greater than 180 degrees or smaller than -180 degrees. - If you imagine the from and to vectors as lines on a piece of paper, both originating from the same point, then the /axis/ vector would point up out of the paper. - The measured angle between the two vectors would be positive in a clockwise direction and negative in an anti-clockwise direction. ___ **Reference:** - github.com angle2d(a, b)   2D angle between two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.   Returns: `float` Angle value in degrees. ___ **Usage:** ``` a = .angle2d(.one(), .left()) ``` transform_Matrix(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (matrix) : `matrix` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` mat = matrix.new(4, 0) mat.add_row(0, array.from(0.0, 0.0, 0.0, 1.0)) mat.add_row(1, array.from(0.0, 0.0, 1.0, 0.0)) mat.add_row(2, array.from(0.0, 1.0, 0.0, 0.0)) mat.add_row(3, array.from(1.0, 0.0, 0.0, 0.0)) b = .transform_Matrix(.one(), mat) ``` transform_M44(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (M44 type from RicardoSantos/CommonTypesMath/1) : `M44` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_M44(.one(), .M44.new(0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0)) ``` ___ **Notes:** - Type `M44` from `CommonTypesMath` library. transform_normal_Matrix(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (matrix) : `matrix` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` mat = matrix.new(4, 0) mat.add_row(0, array.from(0.0, 0.0, 0.0, 1.0)) mat.add_row(1, array.from(0.0, 0.0, 1.0, 0.0)) mat.add_row(2, array.from(0.0, 1.0, 0.0, 0.0)) mat.add_row(3, array.from(1.0, 0.0, 0.0, 0.0)) b = .transform_normal_Matrix(.one(), mat) ``` transform_normal_M44(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (M44 type from RicardoSantos/CommonTypesMath/1) : `M44` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_normal_M44(.one(), .M44.new(0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0)) ``` ___ **Notes:** - Type `M44` from `CommonTypesMath` library. transform_Array(a, rotation)   Transforms a vector by the given Quaternion rotation value.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector. The source vector to be rotated.      rotation (float ) : `array` A 4 element array. Quaternion. The rotation to apply.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_Array(.one(), array.from(0.2, 0.2, 0.2, 1.0)) ``` ___ **Reference:** - referencesource.microsoft.com transform_Quaternion(a, rotation)   Transforms a vector by the given Quaternion rotation value.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector. The source vector to be rotated.      rotation (Quaternion type from RicardoSantos/CommonTypesMath/1) : `array` A 4 element array. Quaternion. The rotation to apply.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_Quaternion(.one(), .Quaternion.new(0.2, 0.2, 0.2, 1.0)) ``` ___ **Notes:** - Type `Quaternion` from `CommonTypesMath` library. ___ **Reference:** - referencesource.microsoft.com
ספריית Pine Script™
מאת ‎RicardoSantos‎
7
Mad_MATHLibrary "MAD_MATH" This is a mathematical library where I store useful kernels, filters and selectors for the different types of computations. This library also contains opensource code from other scripters. Future extensions are very likely, there are some functions I would like to add, but I have to wait for approvals so i can include them. Ehlers_EMA(_src, _length)   Calculates the Ehlers Exponential Moving Average (Ehlers_EMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers EMA   Returns: The Ehlers EMA value Ehlers_Gaussian(_src, _length)   Calculates the Ehlers Gaussian Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Gaussian Filter   Returns: The Ehlers Gaussian Filter value Ehlers_supersmoother(_src, _length)   Calculates the Ehlers Supersmoother   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Supersmoother   Returns: The Ehlers Supersmoother value Ehlers_SMA_fast(_src, _length)   Calculates the Ehlers Simple Moving Average (SMA) Fast   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers SMA Fast   Returns: The Ehlers SMA Fast value Ehlers_EMA_fast(_src, _length)   Calculates the Ehlers Exponential Moving Average (EMA) Fast   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers EMA Fast   Returns: The Ehlers EMA Fast value Ehlers_RSI_fast(_src, _length)   Calculates the Ehlers Relative Strength Index (RSI) Fast   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers RSI Fast   Returns: The Ehlers RSI Fast value Ehlers_Band_Pass_Filter(_src, _length)   Calculates the Ehlers BandPass Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers BandPass Filter   Returns: The Ehlers BandPass Filter value Ehlers_Butterworth(_src, _length)   Calculates the Ehlers Butterworth Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Butterworth Filter   Returns: The Ehlers Butterworth Filter value Ehlers_Two_Pole_Gaussian_Filter(_src, _length)   Calculates the Ehlers Two-Pole Gaussian Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Two-Pole Gaussian Filter   Returns: The Ehlers Two-Pole Gaussian Filter value Ehlers_Two_Pole_Butterworth_Filter(_src, _length)   Calculates the Ehlers Two-Pole Butterworth Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Two-Pole Butterworth Filter   Returns: The Ehlers Two-Pole Butterworth Filter value Ehlers_Band_Stop_Filter(_src, _length)   Calculates the Ehlers Band Stop Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Band Stop Filter   Returns: The Ehlers Band Stop Filter value Ehlers_Smoother(_src)   Calculates the Ehlers Smoother   Parameters:      _src (float) : The source series for calculation   Returns: The Ehlers Smoother value Ehlers_High_Pass_Filter(_src, _length)   Calculates the Ehlers High Pass Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers High Pass Filter   Returns: The Ehlers High Pass Filter value Ehlers_2_Pole_High_Pass_Filter(_src, _length)   Calculates the Ehlers Two-Pole High Pass Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Two-Pole High Pass Filter   Returns: The Ehlers Two-Pole High Pass Filter value pr(_src, _length)   pr Calculates the percentage rank (PR) of a value within a range.   Parameters:      _src (float) : The source value for which the percentage rank is calculated. It represents the value to be ranked within the range.      _length (simple int) : The _length of the range over which the percentage rank is calculated. It determines the number of bars considered for the calculation.   Returns: The percentage rank (PR) of the source value within the range, adjusted by adding 50 to the result. smma(_src, _length)   Calculates the SMMA (Smoothed Moving Average)   Parameters:      _src (float) : The source series for calculation      _length (simple int)   Returns: The SMMA value hullma(_src, _length)   Calculates the Hull Moving Average (HullMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the HullMA   Returns: The HullMA value tma(_src, _length)   Calculates the Triple Moving Average (TMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the TMA   Returns: The TMA value dema(_src, _length)   Calculates the Double Exponential Moving Average (DEMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the DEMA   Returns: The DEMA value tema(_src, _length)   Calculates the Triple Exponential Moving Average (TEMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the TEMA   Returns: The TEMA value w2ma(_src, _length)   Calculates the Normalized Double Moving Average (N2MA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the N2MA   Returns: The N2MA value wma(_src, _length)   Calculates the Normalized Moving Average (NMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the NMA   Returns: The NMA value nma(_open, _close, _length)   Calculates the Normalized Moving Average (NMA)   Parameters:      _open (float) : The open price series      _close (float) : The close price series      _length (simple int) : The _length for finding the highest and lowest values   Returns: The NMA value lma(_src, _length)   Parameters:      _src (float)      _length (simple int) zero_lag(_src, _length, gamma1, zl)   Calculates the Zero Lag Moving Average (ZeroLag)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average      gamma1 (simple int) : The coefficient for calculating 'd'      zl (simple bool) : Boolean flag for applying Zero Lag   Returns: An array containing the ZeroLag Moving Average and a boolean flag indicating if it's flat copyright HPotter, thanks for that great function chebyshevI(src, len, ripple)   Calculates the Chebyshev Type I Filter   Parameters:      src (float) : The source series for calculation      len (int) : The length of the filter      ripple (float) : The ripple factor for the filter   Returns: The output of the Chebyshev Type I Filter math from Pafnuti Lwowitsch Tschebyschow (1821–1894) Thanks peacefulLizard50262 for the find and translation chebyshevII(src, len, ripple)   Calculates the Chebyshev Type II Filter   Parameters:      src (float) : The source series for calculation      len (int) : The length of the filter      ripple (float) : The ripple factor for the filter   Returns: The output of the Chebyshev Type II Filter math from Pafnuti Lwowitsch Tschebyschow (1821–1894) Thanks peacefulLizard50262 for the find wavetrend(_src, _n1, _n2)   Calculates the WaveTrend indicator   Parameters:      _src (float) : The source series for calculation      _n1 (simple int) : The period for the first EMA calculation      _n2 (simple int) : The period for the second EMA calculation   Returns: The WaveTrend value f_getma(_type, _src, _length, ripple)   Calculates various types of moving averages   Parameters:      _type (simple string) : The type of indicator to calculate      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average or indicator      ripple (simple float)   Returns: The calculated moving average or indicator value f_getfilter(_type, _src, _length)   Calculates various types of filters   Parameters:      _type (simple string) : The type of indicator to calculate      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average or indicator   Returns: The filtered value f_getoszillator(_type, _src, _length)   Calculates various types of Deviations and other indicators   Parameters:      _type (simple string) : The type of indicator to calculate      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average or indicator   Returns: The calculated moving average or indicator value
ספריית Pine Script™
מאת ‎djmad‎
5
biased_price_targetLibrary "biased_price_target" Collection of functions that can be used for the calculation of biased price targets like stop loss and take profit from a reference price using several methods that are already provided by the "distance_ratio" library plus the 'HHLL'. Methods supported are percentagewise (PERC), atr-based (ATR), fixed profit (PROF), tick-based (TICKS), risk reward ratio (RR), and highest high/lowest low (HHLL)
ספריית Pine Script™
מאת ‎jason5480‎
3
Mad_StandardpartsLibrary "Mad_Standardparts" This are my Standardparts used in upcoming scipts roundTo(_value, _decimals)   Round a floating point value to a specified number of decimal places. @description This function takes a floating point value and rounds it to a specified number of decimal places.   Parameters:      _value (float) : The floating point value to be rounded.      _decimals (int) : The number of decimal places to round to. Must be a non-negative integer.   Returns: The rounded value, as a floating point number. clear_all()   Delete all drawings on the chart. @description This function deletes all drawings on the chart, including lines, boxes, and labels.   Returns: None. shifting(_value)   Create a string of spaces to shift text over by a specified amount. @description This function takes an integer value and returns a string consisting of that many spaces, which can be used to shift text over in a PineScript chart.   Parameters:      _value (int) : The number of spaces to create in the output string.   Returns: A string consisting of the specified number of spaces. fromLog(_value)   Convert a linear value to a logarithmic value. @description This function takes a linear value and converts it to a logarithmic value, using the formula specified in the code.   Parameters:      _value (float)   Returns: The corresponding logarithmic value, as a floating point number. toLog(_value)   Convert a logarithmic value to a linear value. @description This function takes a logarithmic value and converts it to a linear value, using the formula specified in the code.   Parameters:      _value (float)   Returns: The corresponding linear value, as a floating point number. f_getbartime()   Calculate the time per bar on the chart. @description This function calculates the time per bar on the chart based on the first 100 bars.   Returns: The time per bar, as an integer value.
ספריית Pine Script™
מאת ‎djmad‎
BenfordsLawLibrary "BenfordsLaw" Methods to deal with Benford's law which states that a distribution of first and higher order digits of numerical strings has a characteristic pattern. "Benford's law is an observation about the leading digits of the numbers found in real-world data sets. Intuitively, one might expect that the leading digits of these numbers would be uniformly distributed so that each of the digits from 1 to 9 is equally likely to appear. In fact, it is often the case that 1 occurs more frequently than 2, 2 more frequently than 3, and so on. This observation is a simplified version of Benford's law. More precisely, the law gives a prediction of the frequency of leading digits using base-10 logarithms that predicts specific frequencies which decrease as the digits increase from 1 to 9." ~(2) --- reference: - 1: en.wikipedia.org - 2: brilliant.org - 4: github.com cumsum_difference(a, b)   Calculate the cumulative sum difference of two arrays of same size.   Parameters:      a (float ) : `array` List of values.      b (float ) : `array` List of values.   Returns: List with CumSum Difference between arrays. fractional_int(number)   Transform a floating number including its fractional part to integer form ex:. `1.2345 -> 12345`.   Parameters:      number (float) : `float` The number to transform.   Returns: Transformed number. split_to_digits(number, reverse)   Transforms a integer number into a list of its digits.   Parameters:      number (int) : `int` Number to transform.      reverse (bool) : `bool` `default=true`, Reverse the order of the digits, if true, last will be first.   Returns: Transformed number digits list. digit_in(number, digit)   Digit at index.   Parameters:      number (int) : `int` Number to parse.      digit (int) : `int` `default=0`, Index of digit.   Returns: Digit found at the index. digits_from(data, dindex)   Process a list of `int` values and get the list of digits.   Parameters:      data (int ) : `array` List of numbers.      dindex (int) : `int` `default=0`, Index of digit.   Returns: List of digits at the index. digit_counters(digits)   Score digits.   Parameters:      digits (int ) : `array` List of digits.   Returns: List of counters per digit (1-9). digit_distribution(counters)   Calculates the frequency distribution based on counters provided.   Parameters:      counters (int ) : `array` List of counters, must have size(9).   Returns: Distribution of the frequency of the digits. digit_p(digit)   Expected probability for digit according to Benford.   Parameters:      digit (int) : `int` Digit number reference in range `1 -> 9`.   Returns: Probability of digit according to Benford's law. benfords_distribution()   Calculated Expected distribution per digit according to Benford's Law.   Returns: List with the expected distribution. benfords_distribution_aprox()   Aproximate Expected distribution per digit according to Benford's Law.   Returns: List with the expected distribution. test_benfords(digits, calculate_benfords)   Tests Benford's Law on provided list of digits.   Parameters:      digits (int ) : `array` List of digits.      calculate_benfords (bool)   Returns: Tuple with: - Counters: Score of each digit. - Sample distribution: Frequency for each digit. - Expected distribution: Expected frequency according to Benford's. - Cumulative Sum of difference: to_table(digits, _text_color, _border_color, _frame_color)   Parameters:      digits (int )      _text_color (color)      _border_color (color)      _frame_color (color)
ספריית Pine Script™
מאת ‎RicardoSantos‎
6
MathEasingFunctionsLibrary "MathEasingFunctions" A collection of Easing functions. Easing functions are commonly used for smoothing actions over time, They are used to smooth out the sharp edges of a function and make it more pleasing to the eye, like for example the motion of a object through time. Easing functions can be used in a variety of applications, including animation, video games, and scientific simulations. They are a powerful tool for creating realistic visual effects and can help to make your work more engaging and enjoyable to the eye. --- Includes functions for ease in, ease out, and, ease in and out, for the following constructs: sine, quadratic, cubic, quartic, quintic, exponential, elastic, circle, back, bounce. --- Reference: easings.net learn.microsoft.com ease_in_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. select(v, formula, effect, bounded)   Parameters:      v (float)      formula (string)      effect (string)      bounded (bool)
ספריית Pine Script™
מאת ‎RicardoSantos‎
5
UtilityLibrary "Utility" dema(src, length)   Parameters:      src (float)      length (simple int) tema(src, length)   Parameters:      src (float)      length (simple int) hma(src, length)   Parameters:      src (float)      length (int) zlema(src, length)   Parameters:      src (float)      length (simple int) stochRSI(src, lengthRSI, lengthStoch, smoothK, smoothD)   Parameters:      src (float)      lengthRSI (simple int)      lengthStoch (int)      smoothK (int)      smoothD (int) slope(src, length)   Parameters:      src (float)      length (int)
ספריית Pine Script™
מאת ‎derekren321‎
loxxfftLibrary "loxxfft" This code is a library for performing Fast Fourier Transform (FFT) operations. FFT is an algorithm that can quickly compute the discrete Fourier transform (DFT) of a sequence. The library includes functions for performing FFTs on both real and complex data. It also includes functions for fast correlation and convolution, which are operations that can be performed efficiently using FFTs. Additionally, the library includes functions for fast sine and cosine transforms. Reference: www.alglib.net fastfouriertransform(a, nn, inversefft)   Returns Fast Fourier Transform   Parameters:      a (float ) : float , An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices.      nn (int) : int, The number of function values. It must be a power of two, but the algorithm does not validate this.      inversefft (bool) : bool, A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT.   Returns: float , Modifies the input array a in-place, which means that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. The transformed data will have real and imaginary parts interleaved, with the real parts at even indices and the imaginary parts at odd indices. realfastfouriertransform(a, tnn, inversefft)   Returns Real Fast Fourier Transform   Parameters:      a (float ) : float , A float array containing the real-valued function samples.      tnn (int) : int, The number of function values (must be a power of 2, but the algorithm does not validate this condition).      inversefft (bool) : bool, A boolean flag that indicates the direction of the transformation (True for inverse, False for direct).   Returns: float , Modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. fastsinetransform(a, tnn, inversefst)   Returns Fast Discrete Sine Conversion   Parameters:      a (float ) : float , An array of real numbers representing the function values.      tnn (int) : int, Number of function values (must be a power of two, but the code doesn't validate this).      inversefst (bool) : bool, A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST.   Returns: float , The output is the transformed array 'a', which will contain the result of the transformation. fastcosinetransform(a, tnn, inversefct)   Returns Fast Discrete Cosine Transform   Parameters:      a (float ) : float , This is a floating-point array representing the sequence of values (time-domain) that you want to transform. The function will perform the Fast Cosine Transform (FCT) or the inverse FCT on this input array, depending on the value of the inversefct parameter. The transformed result will also be stored in this same array, which means the function modifies the input array in-place.      tnn (int) : int, This is an integer value representing the number of data points in the input array a. It is used to determine the size of the input array and control the loops in the algorithm. Note that the size of the input array should be a power of 2 for the Fast Cosine Transform algorithm to work correctly.      inversefct (bool) : bool, This is a boolean value that controls whether the function performs the regular Fast Cosine Transform or the inverse FCT. If inversefct is set to true, the function will perform the inverse FCT, and if set to false, the regular FCT will be performed. The inverse FCT can be used to transform data back into its original form (time-domain) after the regular FCT has been applied.   Returns: float , The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array. fastconvolution(signal, signallen, response, negativelen, positivelen)   Convolution using FFT   Parameters:      signal (float ) : float , This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1.      signallen (int) : int, This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array.      response (float ) : float , This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively.      negativelen (int) : int, This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero.      positivelen (int) : int, This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero.   Returns: float , The resulting convolved values are stored back in the input signal array. fastcorrelation(signal, signallen, pattern, patternlen)   Returns Correlation using FFT   Parameters:      signal (float ) : float ,This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1.      signallen (int) : int, This is an integer representing the length of the input signal array.      pattern (float ) : float , This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1.      patternlen (int) : int, This is an integer representing the length of the pattern array.   Returns: float , The signal array containing the correlation values at points from 0 to SignalLen-1. tworealffts(a1, a2, a, b, tn)   Returns Fast Fourier Transform of Two Real Functions   Parameters:      a1 (float ) : float , An array of real numbers, representing the values of the first function.      a2 (float ) : float , An array of real numbers, representing the values of the second function.      a (float ) : float , An output array to store the Fourier transform of the first function.      b (float ) : float , An output array to store the Fourier transform of the second function.      tn (int) : float , An integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition.   Returns: float , The a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b. █ Detailed explaination of each function Fast Fourier Transform The fastfouriertransform() function takes three input parameters: 1. a: An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices. 2. nn: The number of function values. It must be a power of two, but the algorithm does not validate this. 3. inversefft: A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT. The function performs the FFT using the Cooley-Tukey algorithm, which is an efficient algorithm for computing the discrete Fourier transform (DFT) and its inverse. The Cooley-Tukey algorithm recursively breaks down the DFT of a sequence into smaller DFTs of subsequences, leading to a significant reduction in computational complexity. The algorithm's time complexity is O(n log n), where n is the number of samples. The fastfouriertransform() function first initializes variables and determines the direction of the transformation based on the inversefft parameter. If inversefft is True, the isign variable is set to -1; otherwise, it is set to 1. Next, the function performs the bit-reversal operation. This is a necessary step before calculating the FFT, as it rearranges the input data in a specific order required by the Cooley-Tukey algorithm. The bit-reversal is performed using a loop that iterates through the nn samples, swapping the data elements according to their bit-reversed index. After the bit-reversal operation, the function iteratively computes the FFT using the Cooley-Tukey algorithm. It performs calculations in a loop that goes through different stages, doubling the size of the sub-FFT at each stage. Within each stage, the Cooley-Tukey algorithm calculates the butterfly operations, which are mathematical operations that combine the results of smaller DFTs into the final DFT. The butterfly operations involve complex number multiplication and addition, updating the input array a with the computed values. The loop also calculates the twiddle factors, which are complex exponential factors used in the butterfly operations. The twiddle factors are calculated using trigonometric functions, such as sine and cosine, based on the angle theta. The variables wpr, wpi, wr, and wi are used to store intermediate values of the twiddle factors, which are updated in each iteration of the loop. Finally, if the inversefft parameter is True, the function divides the result by the number of samples nn to obtain the correct inverse FFT result. This normalization step is performed using a loop that iterates through the array a and divides each element by nn. In summary, the fastfouriertransform() function is an implementation of the Cooley-Tukey FFT algorithm, which is an efficient algorithm for computing the DFT and its inverse. This FFT library can be used for a variety of applications, such as signal processing, image processing, audio processing, and more. Feal Fast Fourier Transform The realfastfouriertransform() function performs a fast Fourier transform (FFT) specifically for real-valued functions. The FFT is an efficient algorithm used to compute the discrete Fourier transform (DFT) and its inverse, which are fundamental tools in signal processing, image processing, and other related fields. This function takes three input parameters: 1. a - A float array containing the real-valued function samples. 2. tnn - The number of function values (must be a power of 2, but the algorithm does not validate this condition). 3. inversefft - A boolean flag that indicates the direction of the transformation (True for inverse, False for direct). The function modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. The algorithm uses a combination of complex-to-complex FFT and additional transformations specific to real-valued data to optimize the computation. It takes into account the symmetry properties of the real-valued input data to reduce the computational complexity. Here's a detailed walkthrough of the algorithm: 1. Depending on the inversefft flag, the initial values for ttheta, c1, and c2 are determined. These values are used for the initial data preprocessing and post-processing steps specific to the real-valued FFT. 2. The preprocessing step computes the initial real and imaginary parts of the data using a combination of sine and cosine terms with the input data. This step effectively converts the real-valued input data into complex-valued data suitable for the complex-to-complex FFT. 3. The complex-to-complex FFT is then performed on the preprocessed complex data. This involves bit-reversal reordering, followed by the Cooley-Tukey radix-2 decimation-in-time algorithm. This part of the code is similar to the fastfouriertransform() function you provided earlier. 4. After the complex-to-complex FFT, a post-processing step is performed to obtain the final real-valued output data. This involves updating the real and imaginary parts of the transformed data using sine and cosine terms, as well as the values c1 and c2. 5. Finally, if the inversefft flag is True, the output data is divided by the number of samples (nn) to obtain the inverse DFT. The function does not return a value explicitly. Instead, the transformed data is stored in the input array a. After the function execution, you can access the transformed data in the a array, which will have the real part at even indices and the imaginary part at odd indices. Fast Sine Transform This code defines a function called fastsinetransform that performs a Fast Discrete Sine Transform (FST) on an array of real numbers. The function takes three input parameters: 1. a (float array): An array of real numbers representing the function values. 2. tnn (int): Number of function values (must be a power of two, but the code doesn't validate this). 3. inversefst (bool): A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST. The output is the transformed array 'a', which will contain the result of the transformation. The code starts by initializing several variables, including trigonometric constants for the sine transform. It then sets the first value of the array 'a' to 0 and calculates the initial values of 'y1' and 'y2', which are used to update the input array 'a' in the following loop. The first loop (with index 'jx') iterates from 2 to (tm + 1), where 'tm' is half of the number of input samples 'tnn'. This loop is responsible for calculating the initial sine transform of the input data. The second loop (with index 'ii') is a bit-reversal loop. It reorders the elements in the array 'a' based on the bit-reversed indices of the original order. The third loop (with index 'ii') iterates while 'n' is greater than 'mmax', which starts at 2 and doubles each iteration. This loop performs the actual Fast Discrete Sine Transform. It calculates the sine transform using the Danielson-Lanczos lemma, which is a divide-and-conquer strategy for calculating Discrete Fourier Transforms (DFTs) efficiently. The fourth loop (with index 'ix') is responsible for the final phase adjustments needed for the sine transform, updating the array 'a' accordingly. The fifth loop (with index 'jj') updates the array 'a' one more time by dividing each element by 2 and calculating the sum of the even-indexed elements. Finally, if the 'inversefst' flag is True, the code scales the transformed data by a factor of 2/tnn to get the inverse Fast Sine Transform. In summary, the code performs a Fast Discrete Sine Transform on an input array of real numbers, either in the direct or inverse direction, and returns the transformed array. The algorithm is based on the Danielson-Lanczos lemma and uses a divide-and-conquer strategy for efficient computation. Fast Cosine Transform This code defines a function called fastcosinetransform that takes three parameters: a floating-point array a, an integer tnn, and a boolean inversefct. The function calculates the Fast Cosine Transform (FCT) or the inverse FCT of the input array, depending on the value of the inversefct parameter. The Fast Cosine Transform is an algorithm that converts a sequence of values (time-domain) into a frequency domain representation. It is closely related to the Fast Fourier Transform (FFT) and can be used in various applications, such as signal processing and image compression. Here's a detailed explanation of the code: 1. The function starts by initializing a number of variables, including counters, intermediate values, and constants. 2. The initial steps of the algorithm are performed. This includes calculating some trigonometric values and updating the input array a with the help of intermediate variables. 3. The code then enters a loop (from jx = 2 to tnn / 2). Within this loop, the algorithm computes and updates the elements of the input array a. 4. After the loop, the function prepares some variables for the next stage of the algorithm. 5. The next part of the algorithm is a series of nested loops that perform the bit-reversal permutation and apply the FCT to the input array a. 6. The code then calculates some additional trigonometric values, which are used in the next loop. 7. The following loop (from ix = 2 to tnn / 4 + 1) computes and updates the elements of the input array a using the previously calculated trigonometric values. 8. The input array a is further updated with the final calculations. 9. In the last loop (from j = 4 to tnn), the algorithm computes and updates the sum of elements in the input array a. 10. Finally, if the inversefct parameter is set to true, the function scales the input array a to obtain the inverse FCT. The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array. Fast Convolution This code defines a function called fastconvolution that performs the convolution of a given signal with a response function using the Fast Fourier Transform (FFT) technique. Convolution is a mathematical operation used in signal processing to combine two signals, producing a third signal representing how the shape of one signal is modified by the other. The fastconvolution function takes the following input parameters: 1. float signal: This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1. 2. int signallen: This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array. 3. float response: This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively. 4. int negativelen: This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero. 5. int positivelen: This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero. The function works by: 1. Calculating the length nl of the arrays used for FFT, ensuring it's a power of 2 and large enough to hold the signal and response. 2. Creating two new arrays, a1 and a2, of length nl and initializing them with the input signal and response function, respectively. 3. Applying the forward FFT (realfastfouriertransform) to both arrays, a1 and a2. 4. Performing element-wise multiplication of the FFT results in the frequency domain. 5. Applying the inverse FFT (realfastfouriertransform) to the multiplied results in a1. 6. Updating the original signal array with the convolution result, which is stored in the a1 array. The result of the convolution is stored in the input signal array at the function exit. Fast Correlation This code defines a function called fastcorrelation that computes the correlation between a signal and a pattern using the Fast Fourier Transform (FFT) method. The function takes four input arguments and modifies the input signal array to store the correlation values. Input arguments: 1. float signal: This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1. 2. int signallen: This is an integer representing the length of the input signal array. 3. float pattern: This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1. 4. int patternlen: This is an integer representing the length of the pattern array. The function performs the following steps: 1. Calculate the required size nl for the FFT by finding the smallest power of 2 that is greater than or equal to the sum of the lengths of the signal and the pattern. 2. Create two new arrays a1 and a2 with the length nl and initialize them to 0. 3. Copy the signal array into a1 and pad it with zeros up to the length nl. 4. Copy the pattern array into a2 and pad it with zeros up to the length nl. 5. Compute the FFT of both a1 and a2. 6. Perform element-wise multiplication of the frequency-domain representation of a1 and the complex conjugate of the frequency-domain representation of a2. 7. Compute the inverse FFT of the result obtained in step 6. 8. Store the resulting correlation values in the original signal array. At the end of the function, the signal array contains the correlation values at points from 0 to SignalLen-1. Fast Fourier Transform of Two Real Functions This code defines a function called tworealffts that computes the Fast Fourier Transform (FFT) of two real-valued functions (a1 and a2) using a Cooley-Tukey-based radix-2 Decimation in Time (DIT) algorithm. The FFT is a widely used algorithm for computing the discrete Fourier transform (DFT) and its inverse. Input parameters: 1. float a1: an array of real numbers, representing the values of the first function. 2. float a2: an array of real numbers, representing the values of the second function. 3. float a: an output array to store the Fourier transform of the first function. 4. float b: an output array to store the Fourier transform of the second function. 5. int tn: an integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition. The function performs the following steps: 1. Combine the two input arrays, a1 and a2, into a single array a by interleaving their elements. 2. Perform a 1D FFT on the combined array a using the radix-2 DIT algorithm. 3. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b. Here is a detailed breakdown of the radix-2 DIT algorithm used in this code: 1. Bit-reverse the order of the elements in the combined array a. 2. Initialize the loop variables mmax, istep, and theta. 3. Enter the main loop that iterates through different stages of the FFT. a. Compute the sine and cosine values for the current stage using the theta variable. b. Initialize the loop variables wr and wi for the current stage. c. Enter the inner loop that iterates through the butterfly operations within each stage. i. Perform the butterfly operation on the elements of array a. ii. Update the loop variables wr and wi for the next butterfly operation. d. Update the loop variables mmax, istep, and theta for the next stage. 4. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b. At the end of the function, the a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b. █ Example scripts using functions contained in loxxfft Real-Fast Fourier Transform of Price w/ Linear Regression Real-Fast Fourier Transform of Price Oscillator Normalized, Variety, Fast Fourier Transform Explorer Variety RSI of Fast Discrete Cosine Transform STD-Stepped Fast Cosine Transform Moving Average
ספריית Pine Script™
מאת ‎loxx‎
3
peterzorve-libraryLibrary "library" is_bullish_engulfing() is_bearish_engulfing() is_hammer(fib_level)   Parameters:      fib_level (float) is_shooting_star(fib_level)   Parameters:      fib_level (float) is_hammer_and_star(fib_level)   Parameters:      fib_level (float) is_star_and_hammer(fib_level)   Parameters:      fib_level (float) is_dogi(dogi_body_ratio)   Parameters:      dogi_body_ratio (float) is_bear_bear_bullish_engulf() is_atr_stoploss_takeprofit(atr_multiplier, atr_length, reward_ratio)   Parameters:      atr_multiplier (float)      atr_length (simple int)      reward_ratio (float) is_fixed_stoploss_takeprofit(stoploss_pips, reward_ratio)   Parameters:      stoploss_pips (float)      reward_ratio (float) is_step_trailing_stoploss(stoploss_pips)   Parameters:      stoploss_pips (float) is_atr_trailing_stoploss(atr_multiplier, break_even_pip)   Parameters:      atr_multiplier (float)      break_even_pip (int) is_pull_back_strategy(length)   Parameters:      length (simple int) is_trade_statistics(condition, entrypoint, stoploss, takeprofit)   Parameters:      condition (bool)      entrypoint (float)      stoploss (float)      takeprofit (float) is_table_of_statistics(win_trades, lost_trades, even_trades, pips_won, pips_lost)   Parameters:      win_trades (int)      lost_trades (int)      even_trades (int)      pips_won (float)      pips_lost (float) is_pine_info(lotsize, stoploss, takeprofit)   Parameters:      lotsize (float)      stoploss (float)      takeprofit (float) is_support_and_resistance_strategy(look_back, look_forward)   Parameters:      look_back (int)      look_forward (int) is_choral_strategy(smoothing_period, constant_d)   Parameters:      smoothing_period (int)      constant_d (float) is_bollinger_band_strategy(length, dev_entry, dev_stoploss, dev_takeprofit)   Parameters:      length (int)      dev_entry (simple float)      dev_stoploss (simple float)      dev_takeprofit (simple float)
ספריית Pine Script™
מאת ‎pet-zor‎
LibAndyLibrary "LibAndy" TODO: add library description here greencandlme(x)   TODO: add function description here   Parameters:      x (float) : TODO: add parameter x description here   Returns: TODO: add what function returns greencandle()   Whether this candle is green or not   Returns: Whether this candle is green or not redcandle()   Whether this candle is red or not   Returns: Whether this candle is red or not boostrapSeries(bsSeries)   Parameters:      bsSeries (float)
ספריית Pine Script™
מאת ‎andym1125‎
AstroLibLibrary "AstroLib", or Astro Library, is a collection of public Pinescript functions & calculations for use in astrology & astronomy indicators. Unless noted otherwise, this library was written jointly by @badsector666 and @BarefootJoey. Library "AstroLib" t_(txt)   Parameters:      txt (string) JDNv2(t, withFraction)   Parameters:      t (float)      withFraction (bool) J2K(t)   Parameters:      t (float) J2KtoUnix(TimeInJDN)   Parameters:      TimeInJDN (float) atan2(y, x)   Parameters:      y (float)      x (float) DegSin(x)   Parameters:      x (float) DegCos(x)   Parameters:      x (float) DegTan(x)   Parameters:      x (float) DegArcsin(x)   Parameters:      x (float) DegArccos(x)   Parameters:      x (float) DegArctan(x)   Parameters:      x (float) DegAtan2(y, x)   Parameters:      y (float)      x (float) range2pi(x)   Parameters:      x (float) range360(x)   Parameters:      x (float) gst(days)   Parameters:      days (float) DegDecimal(Degrees, Minutes, Seconds)   Parameters:      Degrees (float)      Minutes (float)      Seconds (float) Rectangular(R, theta, phi, Index)   Parameters:      R (float)      theta (float)      phi (float)      Index (float) rLength(x, y, z)   Parameters:      x (float)      y (float)      z (float) spherical(x, y, z, Index)   Parameters:      x (float)      y (float)      z (float)      Index (float) obliquity(d)   Parameters:      d (float) requatorial(x, y, z, d, Index)   Parameters:      x (float)      y (float)      z (float)      d (float)      Index (float) recliptic(x, y, z, d, Index)   Parameters:      x (float)      y (float)      z (float)      d (float)      Index (float) sequatorial(R, theta, phi, d, Index)   Parameters:      R (float)      theta (float)      phi (float)      d (float)      Index (float) secliptic(R, theta, phi, d, Index)   Parameters:      R (float)      theta (float)      phi (float)      d (float)      Index (float) precess(d1, d2, DEC, RA, Index, ddec, dra)   Parameters:      d1 (float)      d2 (float)      DEC (float)      RA (float)      Index (float)      ddec (float)      dra (float) riset(J2000, DEC, RA, GLat, GLong, Index)   Parameters:      J2000 (float)      DEC (float)      RA (float)      GLat (float)      GLong (float)      Index (float) ssun(d, Index)   Parameters:      d (float)      Index (float) rsun(d, Index)   Parameters:      d (float)      Index (float) sun(d, Index)   Parameters:      d (float)      Index (float) SunLongitude(d, Index)   Parameters:      d (float)      Index (float) Sunrise(J2000, GLat, GLong, Index, altitudex)   Parameters:      J2000 (float)      GLat (float)      GLong (float)      Index (float)      altitudex (float) smoon(dx, Index)   Parameters:      dx (float)      Index (float) rmoon(d, Index)   Parameters:      d (float)      Index (float) tmoon(d, GLat, GLong, Index)   Parameters:      d (float)      GLat (float)      GLong (float)      Index (float) moon(d, Index)   Parameters:      d (float)      Index (float) Element(d, pnum)   Parameters:      d (float)      pnum (int) kepler(m, ecc, eps)   Parameters:      m (float)      ecc (float)      eps (float) rplanet(d, pnumber, Index)   Parameters:      d (float)      pnumber (int)      Index (float) planet(d, pnumber, Index)   Parameters:      d (float)      pnumber (int)      Index (float) altaz(d, DEC, RA, GLat, GLong, Index)   Parameters:      d (float)      DEC (float)      RA (float)      GLat (float)      GLong (float)      Index (float) prise(d, P, GLat, GLong, Index)   Parameters:      d (float)      P (int)      GLat (float)      GLong (float)      Index (float) MoonSize(d)   Parameters:      d (float) Refraction(Temperature_C, Atmospheric_Pressure_mBar, Altitude_Deg)   Parameters:      Temperature_C (float)      Atmospheric_Pressure_mBar (float)      Altitude_Deg (float) MoonRise(d, Longitude, Latitude, Index)   Parameters:      d (float)      Longitude (float)      Latitude (float)      Index (float) f_to_sec(dec)   Parameters:      dec (float) f_to_time(sec)   Parameters:      sec (float) deg_to_time(deg)   Parameters:      deg (float) toDMS(coordinate)   Parameters:      coordinate (float) convertDMS(lat, lng)   Parameters:      lat (float)      lng (float) convlatdec(deg)   Parameters:      deg (float) PlanetName(pnum)   Parameters:      pnum (int) PlanetNameV(pnum)   Parameters:      pnum (int) PlanetSign(pnum)   Parameters:      pnum (int) PlanetColor(pnum)   Parameters:      pnum (int) zodiaccolor(deg)   Parameters:      deg (float) degsign(deg)   Parameters:      deg (float) degsignf(deg)   Parameters:      deg (float) degnash(deg)   Parameters:      deg (float) degname(deg)   Parameters:      deg (float) retrogradesym(deg)   Parameters:      deg (float) degaspsign(deg)   Parameters:      deg (float) degaspname(deg)   Parameters:      deg (float) degaspfull(deg)   Parameters:      deg (float) degaspfullV2(deg)   Parameters:      deg (float) degaspnameV2(deg)   Parameters:      deg (float) degtolowest180(deg)   Parameters:      deg (float) degaspfullapproach(deg)   Parameters:      deg (float) virinchiaspectcol(deg, bull_col, bear_col)   Parameters:      deg (float)      bull_col (color)      bear_col (color) virinchiaspectemo(deg, bull_emo, bear_emo)   Parameters:      deg (float)      bull_emo (string)      bear_emo (string) aspectfastsigndeg(deg)   Parameters:      deg (float) aspectfastfull(deg)   Parameters:      deg (float) aspectslowfull(deg)   Parameters:      deg (float) aspectslowsigndeg(deg)   Parameters:      deg (float) aspectslowsign(deg)   Parameters:      deg (float) aspectsignprecision(deg, precision)   Parameters:      deg (float)      precision (int) aspectsignprecisionV2(deg, precision)   Parameters:      deg (float)      precision (float) aspectsignprecisionV2ext(deg, precision)   Parameters:      deg (float)      precision (float) IPaspectsignprecision(planet1, planet2, precision)   Parameters:      planet1 (float)      planet2 (float)      precision (float) IPaspectsignprecisionFull(planet1, planet2, precision)   Parameters:      planet1 (float)      planet2 (float)      precision (float) IPaspectlineprecision(planet1, planet2, precision, style, width)   Parameters:      planet1 (float)      planet2 (float)      precision (float)      style (string)      width (int) rDeg(deg)   Parameters:      deg (float) AngToCirc(angle)   Parameters:      angle (float) AngToCirc180(angle)   Parameters:      angle (float) sidereal(deg, sidereal)   Parameters:      deg (float)      sidereal (bool) J2000(JDN)   Parameters:      JDN (float) JDN(t, d, tz)   Parameters:      t (float)      d (float)      tz (float) getsun(index, day, dayr, latitude, longitude, tz)   Parameters:      index (int)      day (float)      dayr (float)      latitude (float)      longitude (float)      tz (float) getmoon(index, day, dayr, latitude, longitude)   Parameters:      index (int)      day (float)      dayr (float)      latitude (float)      longitude (float) getplanet(planet, index, day, dayr, latitude, longitude, tz)   Parameters:      planet (int)      index (int)      day (float)      dayr (float)      latitude (float)      longitude (float)      tz (float)
ספריית Pine Script™
מאת ‎BarefootJoey‎
17
FunctionBaumWelchLibrary "FunctionBaumWelch" Baum-Welch Algorithm, also known as Forward-Backward Algorithm, uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors. --- ### Function List: > `forward (array pi, matrix a, matrix b, array obs)` > `forward (array pi, matrix a, matrix b, array obs, bool scaling)` > `backward (matrix a, matrix b, array obs)` > `backward (matrix a, matrix b, array obs, array c)` > `baumwelch (array observations, int nstates)` > `baumwelch (array observations, array pi, matrix a, matrix b)` --- ### Reference: > en.wikipedia.org > github.com > en.wikipedia.org > www.rdocumentation.org > www.rdocumentation.org forward(pi, a, b, obs)   Computes forward probabilities for state `X` up to observation at time `k`, is defined as the probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`.   Parameters:      pi (float ) : Initial probabilities.      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states.      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given state matrix is size (M x O) where M is number of states and O is number of different possible observations.      obs (int ) : List with actual state observation data.   Returns: - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. forward(pi, a, b, obs, scaling)   Computes forward probabilities for state `X` up to observation at time `k`, is defined as the probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`.   Parameters:      pi (float ) : Initial probabilities.      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states.      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given state matrix is size (M x O) where M is number of states and O is number of different possible observations.      obs (int ) : List with actual state observation data.      scaling (bool) : Normalize `alpha` scale.   Returns: - #### Tuple with: > - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. > - `array _c`: Array with normalization scale. backward(a, b, obs)   Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`.   Parameters:      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations      obs (int ) : Array with actual state observation data.   Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. backward(a, b, obs, c)   Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`.   Parameters:      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations      obs (int ) : Array with actual state observation data.      c (float ) : Array with Normalization scaling coefficients.   Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. baumwelch(observations, nstates)   **(Random Initialization)** Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.   Parameters:      observations (int ) : List of observed states.      nstates (int)   Returns: - #### Tuple with: > - `array _pi`: Initial probability distribution. > - `matrix _a`: Transition probability matrix. > - `matrix _b`: Emission probability matrix. --- requires: `import RicardoSantos/WIPTensor/2 as Tensor` baumwelch(observations, pi, a, b)   Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.   Parameters:      observations (int ) : List of observed states.      pi (float ) : Initial probaility distribution.      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations   Returns: - #### Tuple with: > - `array _pi`: Initial probability distribution. > - `matrix _a`: Transition probability matrix. > - `matrix _b`: Emission probability matrix. --- requires: `import RicardoSantos/WIPTensor/2 as Tensor`
ספריית Pine Script™
מאת ‎RicardoSantos‎
7